arXiv subjects

Chang Liu

Publications and source records attributed to Chang Liu.

At least 127 records · Page 7Linked to original sources

arXiv2026

Fast Implicit Neural Light Field Representation via Geometric Decomposition and Multi-Resolution Low-Rank Features

Implicit neural representations provide a compact and continuous way to reconstruct dense light fields from sampled ray coordinates. However, fast light field reconstruction remains challenging because a light field is a high-dimensional signal with strong spatial-angular redundancy and structured disparity variations. Directly fitting 4D ray coordinates with a neural network often requires considerable optimization time to recover both view appearance and cross-view consistency. To address this issue, this paper proposes a fast implicit light field representation based on geometric decomposition and multi-resolution low-rank features. The proposed method decomposes a 4D light field into a horizontal disparity plane, a spatial texture plane, and a vertical disparity plane. Each plane is represented by a low-rank structure that combines a low-resolution 2D grid with the element-wise product of two high-resolution 1D line features at multiple resolution levels. The fused features are decoded by a lightweight multilayer perceptron to predict RGB values. Experiments on public light field datasets show that the proposed method achieves competitive reconstruction quality while providing a better trade-off among model parameters, training time, and inference efficiency.

cs.CV↗

arXiv2026

SkillShapley: Boundary-Adaptive Shapley Valuation for Skill Step Attribution in LLM Agents

Agent skills are crucial external instructions that enable language agents to execute long procedural tasks such as coding or document processing. Existing agent skills are primarily created through human manual crafting or agent execution traces, with limited understanding of how each step contributes to overall skill performance on specific tasks; i.e., there remains an open problem in quantifying the contribution of individual steps within an agent skill. To address this issue, we first model skill-step attribution as a Shapley value-based contribution estimation problem, and then propose SkillShapley, a step-level attribution framework for agent skills. Notably, SkillShapley operates in two phases, motivated by key empirical insights, i.e., discretized benchmark rewards that create sharp performance cliffs, and step interactions that are largely additive rather than synergistic. Specifically, it first identifies informative coalitional regions, and then adaptively samples new coalitions that can yield reusable marginal evidence. Experiments on skills from the widely adopted SkillsBench demonstrate that our SkillShapley can effectively and efficiently identify high- or low-value skill steps, providing several key takeaways for agent skill creation.

cs.AI↗

arXiv2026

JWST Spectroscopy of Type Ia Supernova 2025rbs from Maximum Light to the Nebular Phase

We present JWST observations of the Type Ia supernova (SN Ia) 2025rbs ($D=$14.5 Mpc) at +1, +23, and +84 days after B-band maximum, spanning peak light through a wavelength-dependent transition toward the nebular phase. Combined with ground-based optical and near-infrared (NIR) data, our panchromatic spectra (0.4-14 $μ$m) include the first maximum-light mid-infrared (MIR) spectrum and the earliest MIR spectroscopic sequence of an SN Ia to date. At peak light, the MIR spectrum exhibits a continuum with permitted and forbidden features, including Si II, Ni II, and early-emerging [Ni III-IV] and [Ar II-III]. By +23 days the MIR is dominated by forbidden lines with a weak continuum, and by +84 days it is fully nebular, whereas the optical/NIR spectra remain transitional. The nebular spectrum reveals strongly stratified ejecta, with stable Ni concentrated at the lowest velocities, radioactive Co at intermediate velocities but absent within ~2000 km s$^{-1}$, and Ar occupying an outer shell. We detect small-scale substructure in [Ca IV] 3.21 $μ$m with fractional amplitudes of a few percent and a characteristic velocity scale of ~800 km s$^{-1}$, which may reflect compositional structure, ionization variations, or both. Radiative-transfer calculations substantially underpredict these MIR Mg II features despite approximately reproducing the NIR Mg II 1.0927 $μ$m line, suggesting that the relative strengths of these transitions are sensitive to the treatment of Mg ionization and excitation. These observations demonstrate that MIR spectroscopy beginning near maximum light simultaneously probes the emerging inner ejecta and rapidly fading outer burning products, providing new constraints for explosion and radiative-transfer models.

astro-ph.HE↗

arXiv2026

200 mm Wafer-Scale Monolithic 3D Integration of Atomic Layer-Deposited Oxide Semiconductors

Monolithic 3D (M3D) integration offers a pathway to overcome the scaling limits of conventional silicon complementary metal-oxide-semiconductor (CMOS) technology by extending dense vertical stacking of multifunctional logic and memory devices. Here, we demonstrate wafer-scale M3D integration of three tiers of atomic-layer-deposited (ALD) indium oxide (InOx)-based devices (>100,000 fabricated), including ferroelectric, enhancement-mode, and depletion-mode field-effect transistors, on 200 mm silicon wafers. We achieve threshold voltage standard deviation as low as 0.04 V, average electron mobility up to 91.6 cm2V-1s-1, and fully functional cross-tier circuits. A four-tier 3D computing-in-memory (CIM) accelerator targeting large language model workloads is developed using a custom InOx process design kit, delivering 1.4x to 2.9x speedup and comparable energy-delay product improvements over 2D baselines. These results establish ALD InOx M3D integration as a scalable and CMOS-compatible platform for next-generation artificial intelligence hardware and advanced electronics.

physics.app-ph↗

arXiv2026

Learning When to See and When to Feel: Adaptive Vision-Torque Fusion for Contact-Aware Manipulation

Vision-based policies have achieved a good performance in robotic manipulation due to the accessibility and richness of visual observations. However, purely visual sensing becomes insufficient in contact-rich and force-sensitive tasks where force/torque (F/T) signals provide critical information about contact dynamics, alignment, and interaction quality. Although various strategies have been proposed to integrate vision and F/T signals, including auxiliary prediction objectives, mixture-of-experts architectures, and contact-aware gating mechanisms, a comparison of these approaches remains lacking. In this work, we provide a controlled comparison of different F/T-vision integration strategies within diffusion-based manipulation policies. In addition, we propose an adaptive integration strategy that ignores F/T signals during non-contact phases while adaptively leveraging both vision and torque information during contact. Experimental results demonstrate that our method outperforms the strongest baseline by 14% in success rate, highlighting the importance of contact-aware multimodal fusion for robotic manipulation.

cs.RO↗

arXiv2026

Near-Infrared and Optical Observations of SN 2024rbc: The First Early Detection of CO and Dust in a Type Ib Supernova

We present optical and near-infrared (NIR) observations of the Type Ib supernova (SN) 2024rbc. Emission from the first CO overtone, resting on a dust continuum at $2.3-2.4$ $μ$m, was observed at 62 days post-explosion. The CO band heads are not resolved, and the emission appears broad, lacking sharp spectral features. This is the first observation of CO in the ejecta of a Type Ib SN reported in literature. The spectra of SN 2024rbc exhibit strong He I lines and numerous neutral and ionized metal lines. Comparing the spectral evolution of SN 2024rbc to other Type Ib, Ic, and IIb SNe indicates it is a Type Ib SN. We compare the velocities of key optical lines to examine the evolution of the ejecta. Additionally, fitting SN light curve models of helium star progenitors computed with the STELLA code to photometric observations indicates a $^{56}$Ni mass of $0.07$ $M_\odot$ and an ejecta mass of $1.7$ $M_\odot$. Fitting a LTE model to the CO overtone implies a CO mass of 5.2 $\times$ 10$^{-4}$ $M_{\odot}$, a CO temperature of $4040$ K, and a CO velocity width of $5905$ km s$^{-1}$. We also fitted a modified blackbody model to the dust continuum, deriving a dust temperature of $910$ K and a dust mass of $1.3$ $\times$ $10^{-3}$ $M_{\odot}$. The CO provides direct evidence for the onset of dust formation, and the observed dust continuum likely originates from newly formed dust in the ejecta. However, robust dust mass estimates require MIR observations.

astro-ph.HE↗

arXiv2026

Khinchin's and Chung's Laws of the Iterated Logarithm at Time Zero for the Linear Stochastic Fractional Diffusion Equation

We consider the linear stochastic fractional diffusion equation \begin{equation*} \partial^β u(t,x)=-\left(-Δ\right)^{α/2}u(t,x) +I_t^γ\bigl[\dot W(t,x)\bigr], \qquad t>0,\quad x\in\mathbb R^d, \end{equation*} with zero initial conditions, where $α>0$, $β\in(0,2)$, and $γ\ge0$. The driving noise $\dot W$ is a centered Gaussian generalized field that is fractional in time and has Riesz-type spatial covariance. For each fixed $x\in\mathbb R^d$, we establish a Khinchin-type law of the iterated logarithm at time zero for the temporal process $t\mapsto u(t,x)$. Under the additional conditions $0\leγ<1$ and $β+γ<2+H$, we also prove the corresponding Chung-type law. The proofs rely on a harmonizable representation, sharp frequency-truncation estimates, an exact small-ball asymptotic, and a localization argument. These results extend the initial-time laws of the iterated logarithm for stochastic heat equations to a broad class of time-fractional stochastic diffusion equations.

math.PR↗

arXiv2026

Emotion Dynamics in Social Deception Games: Analysis of Professional and Nonprofessional Players through Electrodermal Activity in Werewolf Games

The development of AI systems capable of emotionally resonant communication remains a significant challenge. This study examines how humans influence emotions in social deception games by comparing professional and non-professional players. We measured electrodermal activity during gameplay to capture physiological emotional responses and analyzed communication patterns during periods of high emotional arousal. Our results revealed distinct communication strategies: professional players maintained persuasion-based approaches under high arousal, while nonprofessional players shifted toward information-focused communication. Statistical analysis confirmed significant differences in expression patterns between expertise levels. Professional players exhibited more stable emotional states during gameplay, indicating better emotional regulation. These findings inform the design of AI systems that can adapt their communication strategies based on recipient characteristics, advancing the development of emotionally intelligent artificial agents.

cs.HC↗

arXiv2026

Transforming Remanufacturing Automation with Large Language Models: A Forward-Looking Analysis with Case Studies

With growing concerns about resource scarcity and environmental degradation, remanufacturing of end-of-life (EoL) products within the circular economy is attracting increasing attention. Remanufacturing can preserve most of the original manufacturing value and materials while transforming EoL products into like-new condition. However, the variability and uncertainty of EoL products make remanufacturing highly dependent on human expertise. Recently, large language models (LLMs) have demonstrated remarkable capabilities in learning from massive, unstructured datasets, generating expert-level output across various tasks, and communicating with humans in natural language for interpretation. These advantages can align closely with the complex demands of remanufacturing, thereby mitigating the reliance on specialized expertise. However, their roles and research progress in this domain remain underexplored. In this paper, we present a forward-looking review and analysis of the role of LLMs in remanufacturing automation, grounded in a brief critical review of existing LLM-related studies relevant to remanufacturing. Building on this foundation, we introduce ReManGPT as a conceptual framework and use three representative case studies to illustrate selected modules of the framework in practical remanufacturing scenarios. We also analyze three representative remanufacturing applications, electric vehicle batteries, electronic waste, and electric motors, to illustrate how the proposed framework could address their domain-specific challenges. Finally, we discuss the current barriers to deploying this framework in practice and outline future research directions, including LLM-assisted human operation and language-action models for robotic automation.

eess.SY↗

arXiv2026

$π$-Attention: Online Efficient Sparse Transformers for Long-Context Modeling

Sparse attention is crucial in long-context Transformers, which restricts each token to a limited neighborhood and thereby reduces the quadratic cost of full self-attention. Local windows capture nearby context effectively, yet they induce a receptive-field bottleneck for dependencies beyond the window, limiting long-range modeling under moderate depth. In this paper, we propose $π$-Attention, an \emph{online efficient} sparse attention operator: as tokens arrive, each step maintains a streaming working set of local neighbors plus a $π$-indexed long-range fetch, fused by an adaptive prior under a shared softmax. Rather than materializing a global sparse mask in advance, $π$-Attention computes attention on the live working set with hierarchy-aware IO. We analyze causal reachability and minimum depth under this online rule, and show per-step cost remains $\mathcal{O}(k)$. Experiments on language modeling, Long Range Arena, and efficiency profiling---across 4K--32K context lengths---show consistent gains over local-window and other sparse baselines, approaching dense attention quality at linear cost.

cs.CL↗

arXiv2026

ZTF SN Ia DR2 follow-up: early excess in Type Ia supernova light curves

There is broad consensus that Type Ia supernovae (SNe Ia) are the thermonuclear explosions of C/O white dwarfs (WDs) in binary systems, but their progenitors and explosion mechanisms remain uncertain. Their earliest light curves probe the outermost ejecta and provide important constraints on the explosion. We analyse the ZTF DR2 sample of SNe Ia to search for objects exhibiting early flux excess (EEx SNe Ia). We employ two complementary approaches: (i) identifying deviations from models without early excess using power-law and SALT2 fits, and (ii) comparing observations with double-detonation (DD) and companion-interaction (CI) models. The latter identify 145 and 199 candidates, respectively, although the methods disagree substantially on both the objects selected and the total number of candidates. Combining all methods, we identify 17 robust EEx SN Ia candidates, including six over-luminous events (five 91T-like and one 03fg-like). The strongest excesses reach ${\sim}10$--17\% of the peak flux. The best candidates have, on average, higher stretch and preferentially occur in lower-mass, bluer host galaxies, suggesting younger stellar populations. We estimate that EEx SNe Ia comprise $\lesssim25\%$ of all SNe Ia, consistent with previous work, with over-luminous subtypes showing a higher relative incidence than normal SNe Ia. Among the DD models, the preferred solutions favour intermediate WD core masses ($1 M_{\odot}$), low shell-burning fractions ($20\%$), and heavier He-burning products ($^{56}$Ni), whereas the CI models show no clear parameter trends. Overall, we find no compelling evidence that either scenario is preferred as the origin of the observed early flux excesses.

astro-ph.CO↗

arXiv2026

MuRA: Multi-Rank Adaptation for Efficient and Effective Test-Time Vision-Language Generalization

Vision-language models exhibit remarkable zero-shot capabilities but suffer significant performance degradation under distribution shifts. While test-time adaptation (TTA) via Low-Rank Adaptation offers a parameter-efficient solution, we identify a fundamental bottleneck in current methods: the reliance on static rank configurations. Because visual inputs inherently possess varying information densities, a fixed rank forces an inevitable optimization compromise, leading to underfitting on complex scenes and overfitting on simple ones. To bridge this gap, we propose Multi-Rank Adaptation (MuRA), a novel framework that dynamically selects and fuses adaptation modules of varying capacities based on token-level visual complexity. MuRA synergizes Multi-Rank Orthogonal Decomposition to provide a superior, knowledge-preserving initialization, and Unified Component Fusion with Continuous Router Updating to sustainably learn semantic-to-rank mappings. Furthermore, we provide rigorous theoretical justifications mathematically proving the necessity and gradient stability of this adaptive mechanism. Crucially, MuRA's dynamic design uniquely thrives at the deepest visual layer, capitalizing on the shortest gradient backpropagation path. Extensive experiments demonstrate that MuRA achieves state-of-the-art accuracy across extensive domain generalization and cross-dataset benchmarks while significantly reducing both computational and memory overhead.

cs.CV↗

arXiv2026

MUSE: A Heterogeneity-Aware Multimedia Search Engine for Mobile SoCs

On-device multimedia retrieval is vital for smartphones, enabling applications like cross-modal semantic search and multimodal personal AI agents. However, realizing efficient retrieval on mobile SoCs remains a critical systems challenge. Unlike text-based search, multimedia applications depend on high-dimensional cross-modal embeddings (often exceeding 1024 dimensions) and continuously expanding media streams (e.g., video lifelogs and ambient audio). Mobile devices must process these intensive workloads under strict latency, energy, and memory constraints. Existing vector retrieval systems, designed primarily for static server datasets, are poorly suited for the dynamic execution models of mobile SoCs, where interactive queries must coexist with continuous background media ingestion and index maintenance. To address these challenges, we propose MUSE, a MUltimedia Search Engine comprehensively co-designed for mobile SoCs. MUSE tackles these issues through an architecture-index co-design. It introduces a three-stage asynchronous execution pipeline that overlaps DMA transfers, NPU matrix computation, and hardware-vector data adaptation to overcome the memory wall of high-dimensional embeddings. Furthermore, it redesigns the Inverted File Index (IVF) and introduces workload-aware heterogeneous scheduling across the CPU, GPU, and NPU for hybrid interactive-and-ingestion workloads. Evaluated on Snapdragon 8-series SoCs using real-world multimodal datasets, MUSE improves query throughput by up to 1.4x at matched recall, achieves up to 7x faster index construction, and delivers up to 6x higher insertion throughput under concurrent streaming. Crucially, its accelerator-native design ensures strict physical compliance, capping peak device temperatures at 38°C and reducing total energy consumption by up to 4.6x compared to CPU-bound baselines.

cs.DC↗

arXiv2026

A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion

Spatial atomic layer deposition (SALD) is a leading atmospheric-pressure, high-throughput route to industrial ALD, but design and control are limited by the cost of predicting surface coverage: high-fidelity CFD is far too slow for operating-window scans, while analytic models miss transport modulation such as the gas curtain. We present a physics-chemistry-informed neural network (PCINN), a hybrid surrogate with CFD-level accuracy at real-time speed: a query returns coverage in about 7 ms, roughly 5x10^4 times faster than a CFD solve, reaching a test R^2_log = 0.998 (leave-one-out R^2_raw = 0.974) from only 30 training cases spanning four orders of magnitude in coverage. The architecture is not a black box: a small network learns only the operating-condition to near-wall concentration closure, while the known surface kinetics is a hard-coded, trainable chemistry layer integrated along the substrate trajectory. This single-scalar bottleneck keeps it accurate under sparse data, interpretable and invertible. We add a full identifiability analysis (Fisher information, profile likelihood). The adsorption energy E_ads and desorption rate k_des are robustly identifiable; k_ads is not separately identifiable at a single temperature (only k_ads*c_wall is). Across four temperatures the prefactor nu and E_ads bind along a weakly identifiable degeneracy valley of slope 0.065 eV/decade, derived analytically as k_B T_eff ln(10) and turned into a reliability diagnostic: a seven-chemistry mismatch matrix shows it is invariant under any single-Arrhenius mismatch and shifts only when a second thermally activated process appears, so a slope departure flags unmodelled site heterogeneity. Data come from simulation with known ground truth inverted by the same kinetic form, so the study verifies pipeline self-consistency and the identifiability boundary, not real parameters.

cs.LG↗

arXiv2026

Temporal Concentration from Rollout Errors: Implicit Preference Optimization for Text-to-Video Diffusion

Recent advances in preference alignment for diffusion-based video generation, particularly via Direct Preference Optimization (DPO), have significantly improved visual quality. However, temporally sparse artifacts such as motion collapse, object flickering, and color oversaturation remain a major barrier to perceptual realism. Existing methods struggle with these issues due to two key limitations: (1) the preference attribution bottleneck, where offline human annotations are costly and fail to accurately capture learning dynamics, while online reward signals are rollout-aware but often unstable and biased; and (2) temporal credit misallocation, where uniformly applied supervision cannot effectively target the brief segments in which artifacts occur. To address these challenges, we propose concentrated Implicit Preference Optimization (cIPO), a post-training framework for video diffusion models. cIPO derives implicit preference signals directly from the denoising process: given a real video, the model adds forward noise and reconstructs it via iterative denoising, treating the original as the preferred sample and the reconstruction as the dispreferred one. This formulation captures inference-time errors without requiring human annotations or external reward models. Moreover, frame-level discrepancies between original and reconstructed videos reveal when failures occur. cIPO leverages this by computing temporal reconstruction errors and concentrating optimization on high-error segments, enabling more precise correction of failure-prone regions. Extensive experiments demonstrate that cIPO consistently enhances video authenticity and temporal coherence across multiple datasets, highlighting the effectiveness and efficiency of implicit preference with temporally concentrated optimization.

cs.CV↗

arXiv2026

Bias Correction of Long-memory Estimator of Functional Time Series via the Prefiltered Sieve Bootstrap

We investigate a bias correction procedure based on sieve bootstrapping to estimate the long-memory parameter d in stationary or nonstationary fractionally integrated processes. The resampling method implements a sieve bootstrap method on data prefiltered by a preliminary estimate of the long-memory parameter. For the initial estimate, we recommend the local polynomial Whittle with noise (LPWN) estimator in Frederiksen et al. (2012) to reduce bias, especially in the presence of a strong short-range autoregressive dependence. Through a series of simulation studies, we first highlight the issue of bias, using the local Whittle or detrended fluctuation analysis estimator. Then, we consider the LPWN estimator and show the potential improvement in bias achieved by its sieve bootstrap enhancement. As a byproduct, the sieve bootstrap can also provide confidence intervals of the memory parameter.

stat.ME↗

arXiv2026

FAVA: Formal Authorization for Verified Agents with Evidence-Backed Permission Graphs

Large language model (LLM) agents autonomously interleave semantic reasoning with complex system operations. In these dynamic environments, static tool-level permissions are fundamentally insufficient; safe authorization is highly context-dependent and heavily reliant on evolving runtime states and data flows. We present FAVA (Formal Authorization for Verified Agents), a permission-carrying authorization framework for agent execution. FAVA utilizes an LLM-guided Permission Intermediate Representation (IR) to translate ambiguous natural-language tasks into structured constraints. A deterministic lowering pass then converts this IR into an evidence-backed permission graph that explicitly tracks data flows, dependencies, and contextual labels. To provide strict security guarantees, a Satisfiability Modulo Theories (SMT) authorizer mathematically verifies the current graph against security policies before any effectful action executes. A runtime gateway then enforces the solver's result, either authorizing the execution or intercepting it with a precise counterexample. We evaluate FAVA across OpenAgentSafety, OctoBench, and ActPlane scenarios. Our evaluation demonstrates that FAVA achieves a 90.5% Decision Compliance Rate (DCR) over the aggregate dataset, successfully intercepting dynamic violating traces in the evaluated trace-conditioned scenarios.

cs.CR↗

arXiv2026

PanDent: Toward Comprehensive Tooth-Level Structure-Language Consistency in Dental Radiology

Accurate evaluation of multimodal large language models (MLLMs) in dental panoramic radiography (orthopantomogram, OPG) is limited by the lack of fine-grained, clinically reliable benchmarks that reflect expert interpretation. This work introduces PanDent, a large-scale, clinically grounded OPG benchmark built upon fine-grained, expert-validated tooth-level annotations. The dataset comprises 9,524 high-quality OPGs, each associated with comprehensive structured annotations produced by experienced dentists and further validated by an oral and maxillofacial radiologist, providing clinically reliable supervision for tooth-level diagnosis and reasoning. Clinically consistent radiology reports are constructed from expert-validated findings using clinician-defined reporting logic, establishing explicit correspondence between structured clinical evidence and free-text descriptions. This design enables evaluation of whether MLLMs generate reports that are not only linguistically coherent but also clinically consistent with expert-validated tooth-level findings. Experiments are conducted on diverse MLLMs, including state-of-the-art (SOTA) proprietary models, general-domain open-source models, and medical-specific models. Results show that current MLLMs can generate fluent reports, yet fail to produce clinically consistent descriptions, exhibiting substantial errors in fine-grained localization and tooth-level diagnosis. Fine-tuning on PanDent significantly improves structure-language consistency, substantially enhancing visual localization accuracy and diagnostic correctness, and bringing model outputs closer to expert dental interpretation. These results establish PanDent as a rigorous benchmark for evaluating tooth-level clinical reasoning in MLLMs and a valuable resource for clinically grounded dental AI.

cs.CV↗